[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121338-en":3,"doc-seo-121338-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121338,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","AI-Powered Fact Verification - Analyzing Public Consensus Through Machine Learning","The proliferation of misinformation on digital platforms necessitates scalable, automated truth-verification systems. This paper introduces a machine learning-based framework that measures alignment between social media posts and declarative statements using a transformer architecture. Multiple classification settings are evaluated, including consensus estimation, sentiment evaluation, and binary or multiclass predictions. Experiments indicate binary classification achieves the highest accuracy, while sentiment-based alignment only partially reflects factual correspondence, limiting reliability for robust misinformation detection.","AI-Powered Fact Verification: Analyzing Public Consensus Through Machine Learning  \nAakash Mor  \nUniversity of the Arts London  \n[aakashmor@gmail.com](aakashmor@gmail.com)  \nAbstract—The proliferation of misinformation on digital platforms necessitates scalable, automated truth-verification systems. This paper presents a machine learning-based framework that analyzes the alignment between social media posts and declarative statements using a transformer-based architecture. By experimenting with different classification paradigms—including consensus estimation, sentiment evaluation, and binary/multiclass predictions—the system’s capabilities and limitations are systematically assessed. Empirical results reveal that binary classification yields the highest accuracy, while sentiment-based alignment, though partially effective, fails to capture factual correspondence reliably. These findings contribute to designing robust, interpretable tools for large-scale misinformation detection.  \nI. INTRODUCTION  \nThe proliferation of digital communication platforms has intensified the necessity for autonomous frameworks capable of evaluating the veracity of online information. The widespread adoption of social networking sites has accelerated the dissemination of both misinformation and disinformation, creating substantial obstacles for communities and authoritative organizations alike. Determining the factual correctness of assertions made within online posts plays a pivotal role in countering the propagation of deceptive content. This manuscript critically examines prior investigations addressing this challenge by implementing machine learning algorithms, with a particular focus on employing the DistilBERT architecture [1], to forecast the authenticity of tweets solely based on their textual components.  \nThe present study aims to reconstruct experimental procedures intended to train predictive models capable of assessing the degree of alignment between a tweet and an accompanying statement—hereafter termed as ”consensus.” The utilized corpus consists of pairs of tweets and statements, annotated with labels reflecting human-determined consensus regarding their agreement. A variety of preprocessing methodologies are scrutinized to optimize data preparation, and their respective impacts on the model’s predictive performance are thoroughly evaluated based on classification accuracy metrics. This work’s primary contribution lies in empirically evaluating various AIbased consensus detection strategies, highlighting the implications of input design and label complexity. Furthermore, it proposes refinements to dataset structuring and evaluation metrics that are applicable to broader fact-checking systems.  \nII. RELATED WORK  \nSaimbhi [2] and Desai [3] have made significant contributions to the applications of AI in software security, media authenticity, and enterprise solutions. Saimbhi’s [4] work in Enhancing Software Vulnerability Detection Using Code Property Graphs and Convolutional Neural Networks introduces an innovative approach to improving vulnerability detection by integrating abstract syntax trees, control flow graphs, and program dependency graphs into code property graphs. This is further enhanced by the use of convolutional neural networks tailored for graph data. In Distinguishing True and Fake Ultra-High Definition Images Using Relative DCT Analysis and Machine Learning, he applies Discrete Cosine Transform (DCT) analysis and machine learning techniques to achieve high accuracy in identifying fake UHD images. Desai, [5] on the other hand, focuses on practical machine learning applications. In Enhancing Inventory Management with Progressive Web Applications (PWAs) , he develops a scalable framework for inventory management with features like barcode scanning and geolocation. Additionally, Desai’s [6] work in Active Learning Strategies for Efficient Text Classification explores selective labeling techniques to enhance classificatio","cbCailR6M61B7BgN","https://ap.wps.com/l/cbCailR6M61B7BgN","pdf",133207,1,6,"English","en",105,"# Introduction\n## Study objectives and approach\n# Related Work\n## DistilBERT-focused tweet authenticity\n# TruthSeeker Dataset\n## Lexical Clarifications","[{\"question\":\"What problem does the paper address in online information?\",\"answer\":\"The paper addresses the need for autonomous frameworks that assess the veracity of online assertions amid widespread misinformation and disinformation on social platforms.\"},{\"question\":\"How does the proposed system verify facts using machine learning?\",\"answer\":\"It trains a machine learning framework to analyze alignment between social media posts (tweets) and accompanying declarative statements using a transformer-based architecture, notably DistilBERT.\"},{\"question\":\"What were the key findings from the classification experiments?\",\"answer\":\"Binary classification produced the highest accuracy, while sentiment-based alignment was partially effective and did not reliably capture factual correspondence.\"}]","AI-Powered Fact Verification - 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